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Representing Uncertainty in RuleML

Damásio C.V., Pan J.Z., Stoilos G., Straccia U.

Fundamenta Informaticae

2008

Vol. 82, nr 3

265-288

The RuleML initiative defines a normalized markup for expressing and exchange rules in the Semantic Web. However, the syntax of the language is still limited and lacks features for representing rule-based languages capable of handling uncertainty and vagueness. It is desirable to have a general extension of RuleML which accommodates major existing languages proposed in the latest two decades. The main contribution of the paper is to propose such a general extension, showing how to encode many of the existing languages in this extension. It is detailed the important case of fuzzy rule languages. We hope this work can also provide some insights on how to cover uncertainty in the RIF framework.

From Rough Sets to Rough Knowledge Bases

Vitória A., Damásio C. V., Małuszyński J.

Fundamenta Informaticae

2003

Vol. 57, nr 2-4

215--246

This paper presents an expressive language for representing knowledge about vague concepts. It is based on the rough set formalism and it caters for implicit definition of rough relations by combining different regions (e.g. upper approximation, lower approximation, boundary) of other rough relations. The semantics of the proposed language is obtained by translating it to the language of extended logic programs whose meaning is captured by paraconsistent stable models. A query language is also discussed to retrieve information about the defined rough relations. Motivating examples illustrating the use of the language are described.